Sökning: L773:0010 4825 OR L773:1879 0534 >
Computer aided dete...
Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
-
- Längkvist, Martin, 1983- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems
-
- Jendeberg, Johan, 1972- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden
-
- Thunberg, Per, 1968- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Department of Medical Physics, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden
-
visa fler...
-
- Loutfi, Amy, 1978- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Center for Applied Autonomous Sensor Systems
-
- Lidén, Mats, 1976- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden
-
visa färre...
-
(creator_code:org_t)
- Elsevier, 2018
- 2018
- Engelska.
-
Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 97, s. 153-160
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Computer aided detection
- Ureteral stone
- Convolutional neural networks
- Computed tomography
- Training set selection
- False positive reduction
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas